224 research outputs found
Coresets for Clustering with General Assignment Constraints
Designing small-sized \emph{coresets}, which approximately preserve the costs
of the solutions for large datasets, has been an important research direction
for the past decade. We consider coreset construction for a variety of general
constrained clustering problems. We significantly extend and generalize the
results of a very recent paper (Braverman et al., FOCS'22), by demonstrating
that the idea of hierarchical uniform sampling (Chen, SICOMP'09; Braverman et
al., FOCS'22) can be applied to efficiently construct coresets for a very
general class of constrained clustering problems with general assignment
constraints, including capacity constraints on cluster centers, and assignment
structure constraints for data points (modeled by a convex body .
Our main theorem shows that a small-sized -coreset exists as long
as a complexity measure of the structure
constraint, and the \emph{covering exponent}
for metric space are bounded. The complexity measure
for convex body is the Lipschitz
constant of a certain transportation problem constrained in ,
called \emph{optimal assignment transportation problem}. We prove nontrivial
upper bounds of for various polytopes, including
the general matroid basis polytopes, and laminar matroid polytopes (with better
bound). As an application of our general theorem, we construct the first
coreset for the fault-tolerant clustering problem (with or without capacity
upper/lower bound) for the above metric spaces, in which the fault-tolerance
requirement is captured by a uniform matroid basis polytope
Three Puzzles on Mathematics, Computation, and Games
In this lecture I will talk about three mathematical puzzles involving
mathematics and computation that have preoccupied me over the years. The first
puzzle is to understand the amazing success of the simplex algorithm for linear
programming. The second puzzle is about errors made when votes are counted
during elections. The third puzzle is: are quantum computers possible?Comment: ICM 2018 plenary lecture, Rio de Janeiro, 36 pages, 7 Figure
Sapo: Reachability Computation and Parameter Synthesis of Polynomial Dynamical Systems
Sapo is a C++ tool for the formal analysis of polynomial dynamical systems.
Its main features are: 1) Reachability computation, i.e., the calculation of
the set of states reachable from a set of initial conditions, and 2) Parameter
synthesis, i.e., the refinement of a set of parameters so that the system
satisfies a given specification. Sapo can represent reachable sets as unions of
boxes, parallelotopes, or parallelotope bundles (symbolic representation of
polytopes). Sets of parameters are represented with polytopes while
specifications are formalized as Signal Temporal Logic (STL) formulas
Fault-tolerance in metric dimension of boron nanotubes lattices
The concept of resolving set and metric basis has been very successful because of multi-purpose applications both in computer and mathematical sciences. A system in which failure of any single unit, another chain of units not containing the faulty unit can replace the originally used chain is called a fault-tolerant self-stable system. Recent research studies reveal that the problem of finding metric dimension is NP-hard for general graphs and the problem of computing the exact values of fault-tolerant metric dimension seems to be even harder although some bounds can be computed rather easily. In this article, we compute closed formulas for the fault-tolerant metric dimension of lattices of two types of boron nanotubes, namely triangular and alpha boron. These lattices are formed by cutting the tubes vertically. We conclude that both tubes have constant fault tolerance metric dimension 4
Notes on the Localization of Generalized Hexagonal Cellular Networks
The act of accessing the exact location, or position, of a node in a network is known as the localization of a network. In this methodology, the precise location of each node within a network can be made in the terms of certain chosen nodes in a subset. This subset is known as the locating set and its minimum cardinality is called the locating number of a network. The generalized hexagonal cellular network is a novel structure for the planning and analysis of a network. In this work, we considered conducting the localization of a generalized hexagonal cellular network. Moreover, we determined and proved the exact locating number for this network. Furthermore, in this technique, each node of a generalized hexagonal cellular network can be accessed uniquely. Lastly, we also discussed the generalized version of the locating set and locating number
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